Latent Class Analysis of Gameplay Metrics from Youth Playing a Robotics Game

Game metrics have become a staple component to understanding how players interact with various aspects of the game and whether they comprehend the game mechanics. Despite their intrinsic value, few studies have used this type of objective instrumentation to examine whether users display unique gameplay styles. We used latent class analysis with seven game metric indicators from a robotics game to ascertain whether there are distinct patterns of gameplay. We also validated gameplay styles using measures of persistence and intensity of play. Four gameplay styles were obtained including Fully Engaged (engaged multiple aspects of the game), Engaged in Training (drove the robot but did not prepare for matches or take tutorials), Engaged in Building (accomplished game objectives, met challenges, and took tutorial), and Engaged in Driving (only drove the robot). Persistence and gameplay intensity were both associated with class membership and the obtained classes differed in mean levels of these measures. This study is unique by using a person-centered approach with game metrics as opposed to lower resolution and less reliable self-reports from players. Findings are discussed in terms of ways game developers can utilize game metrics to improve robotics game design and enhance game mechanics.