Serious games present a unique opportunity to educate and train users in risk-free environments. However, traditional assessment methods, such as pre-test and post-test questionnaires, often fail to capture the nuances of the learning process. This paper explores , an emerging field that transforms gameplay data into actionable intelligence for performance measurement and gameplay optimization . We examine methodologies such as Game Learning Analytics (GLA) , stealth assessment, and AI-driven real-time feedback systems to evaluate how they enhance pedagogical effectiveness and player engagement. 1. Introduction
Evaluating performance in serious games requires moving beyond static results to analyze the —sequences of interactions that indicate skill development. Serious Games Analytics: Methodologies for Perf...
Serious Games Analytics: Methodologies for Performance Evaluation and Gameplay Improvement Serious games present a unique opportunity to educate
Serious games are digital applications designed for primary purposes other than pure entertainment, such as education, healthcare, or workforce training. While effective, stakeholders often lack precise metrics to determine if a player is truly learning or simply "gaming the system" by finding loopholes. addresses this by applying statistical models and data mining to trace user-generated data, providing specific strategies for retraining and performance improvement. 2. Methodologies for Performance Evaluation We examine methodologies such as Game Learning Analytics