A Skill Tracing Model for Player Character Control in STG

Abstract

STGs, a longstanding video game subgenre, have grown more intricate over time, deterring new players. To address this, a training system is required to improve character control skills in STG games. Bayesian Knowledge Tracing (BKT) is a common approach researchers use to monitor and assess students' progress. While BKT is effective in evaluating intellectual knowledge, it falls short in assessing character control skills in STG, a form of motion knowledge. This study proposes a Skill Tracing (ST) model that combines BKT approaches to monitor both cognitive knowledge and character control abilities. Results indicate its superiority in skill-tracking tasks over traditional BKT, offering a more accurate prediction of players' skill levels.