Modeling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game. (August 2015) - Shute, Valerie J.; D'Mello, Sidney; Baker, Ryan; Cho, Kyunghwa; Bosch, Nigel; Ocumpaugh, Jaclyn; Ventura, Matthew; Almeda, Victoria
Abstract: This study investigated the relationships among incoming knowledge, persistence, affective states, in-game progress, and consequently learning outcomes for students using the game Physics Playground. We used structural equation modeling to examine these relations. We tested three models, obtaining a model with good fit to the data. We found evidence that both the pretest and the in-game measure of student performance significantly predicted learning outcome, while the in-game measure of performance was predicted by pretest data, frustration, and engaged concentration. Moreover, we found evidence for two indirect paths from engaged concentration and frustration to learning, via the in-game progress measure. We discuss the importance of these findings, and consider viable next steps concerning the design of effective learning supports within game environments. Highlights: We model relations among various student variables and learning outcome in a game. Pretest and in-game performance significantly predict learning outcome. In-game performance is predicted by pretest data, frustration, and engagement. Two indirect paths involving frustration and engagement predict learning.