Leaderboards and Social Mechanics

This section covers AWorld Lab's social and competitive mechanics — the features that add a community dimension to the engagement experience. Leaderboards turn point accumulation into a dynamic competitive experience, groups enable team-based targeting, and comprehensive logging provides measurable progress visibility. For individual goal-tracking mechanics including missions and streaks, see the missions and progression documentation. For the foundational activities and content, refer to the activities documentation.

Leaderboards

AWorld Lab's leaderboard system turns virtual currency accumulation into a dynamic competitive experience, allowing users to compare scores in real time. The system is built on a two-layer architecture that separates what is ranked from when and how it is displayed.

Two-Layer Architecture: Configuration and Runtime

The leaderboard system consists of two distinct components:

Leaderboard Configuration: a template that defines what is ranked — which users are included, which metrics are aggregated, and how scores are calculated.

Runtime Leaderboard: an instance that defines when the ranking applies — the timeframe, recurrence, and current state.

This separation allows a single configuration to power multiple runtime instances. For example, a "Top Points This Week" configuration can automatically generate a new weekly leaderboard instance every Monday, each with its own independent rankings.

Leaderboard Configuration

Each configuration defines a query specification that determines how rankings are computed:

User selection: which users are eligible for the leaderboard, filtered by any user attribute or tag.

Metric aggregation: which data to aggregate — typically virtual currency transactions, but configurable to include other metrics.

Score computation: how the final score is calculated from aggregated data.

Ranking: the order in which users are ranked (typically by score, descending).

Additional display settings include:

Top N: the number of users to display (configurable from 1 to 100).

Show rank: whether to display the user's numerical position.

Show score: whether to display the computed score value.

Runtime Instances: Timeframes and Recurrence

Each runtime leaderboard instance operates within a defined timeframe:

Permanent: the leaderboard runs indefinitely with a cumulative ranking.

Range: the leaderboard is active within a specific start and end date — ideal for campaigns or events.

Recurring: the leaderboard automatically resets at regular intervals.

Recurring leaderboards support multiple recurrence patterns:

Daily: rankings reset every day.

Weekly: rankings reset every week.

Monthly: rankings reset every month.

Custom: recurrence defined by a custom schedule.

Each instance follows a state lifecycle: Pending (before start), Active (accepting and computing rankings), and Ended (final rankings frozen).

Computation Model

Leaderboard scores are computed at query time using aggregation queries against the platform's analytical data store. This approach ensures that:

Rankings always reflect the most current data without synchronization delays.

Configurations can define arbitrary scoring formulas — not just simple sums, but weighted calculations, filtered aggregations, and custom expressions.

The system can be backed by different computation engines depending on the deployment requirements, from SQL-based analytical stores for flexibility to in-memory databases for high-frequency update scenarios.

Segmentation and Display

Rather than offering fixed leaderboard types, AWorld Lab's configuration model enables any segmentation through query definitions:

Global leaderboards: include all users in the workspace, encouraging broad competition.

Community leaderboards: filter users by group tags, fostering competition among peers — such as departments, teams, or geographic regions.

Mission leaderboards: scope rankings to users participating in a specific mission, with a timeframe matching the mission's duration.

Custom segments: any combination of user attributes and tags can define a leaderboard audience.

This flexibility allows clients to create targeted competitive experiences for every context, from company-wide challenges to intimate team competitions.

Groups and User Clusters

AWorld Lab provides a tag-based grouping system that allows clients to organize users into distinct clusters based on attributes, roles, or organizational criteria.

Tag-Based Grouping

Tags can represent any meaningful classification: departments, project teams, user tiers, geographic regions, or participation levels. The system supports assigning multiple tags to each user, enabling flexible and overlapping group structures. For detailed information about the tag model, see the tags and entity targeting section in the missions documentation.

Group-Targeted Mechanics

This grouping mechanism integrates directly with the platform's competitive features:

Group missions: missions assigned to users matching specific tag combinations, enabling differentiated engagement paths for different segments.

Community leaderboards: leaderboards scoped to users within a specific group, fostering competition among peers rather than across the entire user base.

Collective progress tracking: clients can measure the aggregate impact and participation of tagged user clusters, providing visibility into team performance.

The grouping system adapts to various organizational models — from internal corporate initiatives segmented by team or department, to public programs targeting specific user categories. Each client retains full control over how groups are defined and how they interact with the broader gamification system.

Progress Tracking and Impact Measurement

A defining feature of AWorld Lab's gamification infrastructure is the ability to monitor user progress in detail and measure the impact of completed actions.

Comprehensive Logging

The platform maintains a complete audit trail across every gamification domain:

Activity Logs: every action performed, with outcomes, values, and timestamps.

Mission Logs: progress updates and completion events for every mission.

Learning Path Logs: start, progress, and completion states for every learning experience.

Learning Group Logs: per-section progress within Learning Paths.

Quiz Logs: every quiz attempt with answers, outcomes, and difficulty.

Slide Logs: content viewing and completion tracking.

Streak Logs: streak status changes and contribution history.

This logging architecture ensures that every user interaction is captured and available for analysis.

Data-Driven Engagement Insights

The comprehensive logging system provides clients with the data needed to:

Measure individual progress: track each user's journey across activities, missions, and learning experiences.

Analyze collective impact: aggregate participation metrics across groups, departments, or the entire user base.

Optimize engagement strategies: identify which missions, content, and reward structures drive the highest participation.

Report on outcomes: generate evidence-based reports on the impact of gamification programs.

Through this layer, the platform not only increases user engagement but also provides detailed metrics on the impact of user actions, turning gamification into a powerful, measurable growth lever for businesses and organizations.

Leaderboards and Social Mechanics

Leaderboards#

Two-Layer Architecture: Configuration and Runtime#

Leaderboard Configuration#

Runtime Instances: Timeframes and Recurrence#

Computation Model#

Segmentation and Display#

Groups and User Clusters#

Tag-Based Grouping#

Group-Targeted Mechanics#

Progress Tracking and Impact Measurement#

Comprehensive Logging#

Data-Driven Engagement Insights#