This project summarizes my work as the Lead System Designer at Age of Learning. The work is distributed across two products: My Math Academy and My Reading Academy. Overall, I am responsible for maintaining and designing new features for the Personalized Mastery Learning Ecosystem (PMLE).
Systems design, Meta-Game design, and Game Design.
– Led the system design efforts at for two products: My Math Academy and My Reading Academy.
– Created nodemap structures to optimize content traversal
– Designed core gameplay loop to enhance retention and engagement
– Developed an algorithm (Xnode-place) to improve player placement within the game ecosystem.
Background & Goal
My Math Academy and My Reading Academy are educational game products for pk-2 learners. The products help build a strong foundation in Math and Reading skills for young learners. The image below shows adaptive hubs presented to the players a snapshot of the network of mini-games for My Reading Academy.
System Design Features
The Personalized Mastery Learning Ecosystem serves as the adaptive backbone for all content across both products. I developed the following features for this product:
Created Nodemap Structures to optimize content traversal: As shown in image above, the nodemap is a vast collection of mini-games and can present significant complexity. To simplify this structure, I developed a layered nodemap heirarchy including modules, nodes, and activities. These layers allowed delegation of responsibility across the nodemap to guide player progression. Modules were connected to game themeing and contextualization. Nodes focused on mastery of specific topics and followed story within a specific context in the game world, while activities focused on the gameplay mechanics and feedback loops to support player experience.
Designed core gameplay loop to enhance player retention and engagement. I designed a streamlined gameplay loop which rewarded players to continue playing within the same module and get to the conclusion of the narrative flow. This design helped players stay within the same topic and game theme, without taking away player agency to choose their own path.
Developed the Xnode-place algorithm to improve player placement: placing players in the right place of their learning trajectory is a key feature provided by the adaptive system. It is important that the system meets the player at their level of understanding to ensure a relevant and engaging first-time user experience. The challenge faced for this feature was the trade-off between time spent assessing players to determine their skill level vs the efficiency of placement determined using pretests. I designed the xnode algorithm guided by the principles of knowledge-space theory, which helped identify the most efficient sequence of pretest to discover sufficient information about the player to place them on the nodemap. This algorithm utilized the prerequisite-based network structure to identify points on the map which provide the most information about the player. This is analogous to identifying hotspots within a asymmetric network of connections. The algorithm is able to reduce the number of pretest required before placement by ~62%. This will help the system quickly get players located on the nodemap and get to playing games more quickly!