Modeling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game

Valerie J. Shute, Sidney K. D'Mello, Ryan Shaun Joazeiro de Baker, Kyunghwa Cho, Nigel Bosch, Jaclyn Ocumpaugh, Matthew Ventura, Ma. Victoria Almeda

Abstract: 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.