Developing ACT-R Model for Key Concept Recall in a Multilayered K-12 Educational Game

Abstract

Educational games, while widely used to enhance engagement and motivation, often struggle to balance instructional content with compelling gameplay. Although integrating learning and gameplay within a unified structure is theoretically effective, it presents practical challenges in achieving both high engagement and instructional impact. To address this, the current study introduces an intertwined Multilayered Educational Game - Computer-based Framework (iMEG C-Framework) and an ACT-R cognitive model to simulate the recall process. These models will be evaluated across three instructional conditions (Traditional Learning, Classic Educational Game, and iMEG) targeting K-12 students in both shortand long-term memory tasks. Cognitive modeling is particularly valuable in K-12 contexts where large-scale studies are often difficult. The iMEG framework separates game mechanics, instructional content, and feedback to create a more adaptive and organized learning experience. ACT-R modeling supports analysis of how students encode, store, and retrieve key concepts, enabling real-time adaptive feedback and instructional refinement. A within-subjects experiment will be conducted with 39 seventh-grade students across three counterbalanced conditions, each involving a 75-minute session on board game design, followed by retention assessments one and seven days later. By combining experimental data with ACT-R modeling, this study explores predictive capabilities and the impact of different game-based learning structures on student trajectories, contributing to the design of motivation-driven learning environments in K-12 education.