Player Performance Estimation Through Adaptive Puzzle Levels Generation in Game-Based Assessment

Abstract

Estimating players’ abilities without compromising alignment with assessment objectives remains a key challenge in developing Game-Based Assessment (GBA). This study proposes a method to estimate player performance through the procedural generation of puzzle game levels that adapt to player achievement. The method integrates Dynamic Difficulty Adjustment (DDA) and player modeling to dynamically align content difficulty with individual abilities while maintaining assessment validity. Unlike most previous PCG-GBA studies, which determine difficulty solely from technical parameters, the proposed method combines cognitive (arithmetic content) and technical (puzzle parameters) indicators, validated through paper-based tests. Implemented in a basic arithmetic puzzle game with sixth-grade primary school students in Indonesia, the approach generated 1,379 gameplay level records. Statistical analysis shows a strong positive correlation between paper-based test scores and the average difficulty of completed levels (r = 0.734, p < 0.001). A low absolute difference between difficulty level and player performance (M = 0.012, SD = 0.028) and a small RMSE (0.031) confirm the accuracy and consistency of difficulty adaptation. The proposed method enables accurate performance estimation through adaptive gameplay, contributing to the development and external validation of PCG-based GBA and pointing toward its potential scalability for personalized educational assessments.