Cross-Game Learning Matching Engine for Balanced Player Skill Pairing
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Solution Overview
Problem
Current video game systems fail to effectively match players of similar achievement levels across different games, leading to unregulated interactions that can disrupt learning experiences, where more advanced players may dominate or become disinterested, while less advanced players may feel frustrated or lost, particularly in educational games.
Innovation Solution
A computerized learning system that assesses user performance across multiple learning applications and uses a matching engine to pair users based on their past performance and selected interaction modes, such as team, mentor, or head-to-head modes, to promote balanced and effective learning interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If players are matched based on individual game performance only, then players can interact within the same game, but players' performance in different games is not considered and learning across multiple games is not optimized
Solution Approach 1:
The matching engine is designed to handle multiple matching modes (head-to-head, cooperative, mentor) and consider performance data from multiple different games simultaneously. The system universally applies the same assessment and matching framework across various learning application programs, enabling cross-game learning optimization without requiring separate matching systems for each game type.
2Productivity
If head-to-head competition mode is used, then players can compete directly, but more learned players may dominate and humiliate less learned players
Solution Approach 1:
The system dynamically adjusts matching parameters based on player performance levels and selected game modes. In head-to-head competitions, the matching engine considers historical performance data and learning trajectories to create balanced matchups that prevent domination while maintaining competitive engagement, thereby preserving learning motivation across different skill levels.
Solution Approach 2:
The system continuously monitors player performance and interaction patterns, using this feedback to refine future matching decisions. When players exhibit frustration or disengagement patterns, the matching engine adjusts subsequent pairings to restore balanced interaction dynamics, ensuring sustained learning motivation.
3Productivity
If cooperative game mode is used, then players can work together, but players of more advanced learning may act before other players, reducing participation opportunities
Solution Approach 1:
In cooperative modes, the matching engine performs preliminary assessment of player skill levels and learning trajectories before forming teams. This preliminary matching ensures that players with different expertise levels are strategically paired to maintain balanced contribution patterns, preventing advanced players from consistently leading and ensuring equitable participation opportunities for all team members.
4Ease of operation
If players are matched without considering learning level, then matching is simple, but players may become disinterested if too easy or frustrated if too difficult
Solution Approach 1:
The matching system automatically performs comprehensive assessment of player performance across multiple games and learning application programs, then autonomously determines optimal matches based on this data. This self-service approach eliminates the need for manual player assessment while ensuring that each player receives appropriately challenging matches that optimize their learning rate without requiring complex user input.
Data Source
AI summary
A system for matching users of learning application programs is provided. As one example, a learning service program can assess the performance of a user based on their interaction with at least one learning application program. Where the user requests an interactive session with other users for a particular learning application program, a learning service program can perform matching of the plurality of users according to the users' assessed abilities in the learning activities involved to achieve multi-user interaction that promotes learning by each of the users.