Digital game-based learning (DGBL) delivers training through video games. Practitioners are using DGBL in attempts to increase motivation,... Show moreDigital game-based learning (DGBL) delivers training through video games. Practitioners are using DGBL in attempts to increase motivation, promote learning, and increase transfer in training. Theory and models of DGBL aim to explain how motivation is created to yield these benefits, and studies have compared DGBL to traditional methods, yet the tenets of these theories remain largely unexamined. The present study tested the process-outcome link of Garris et al.’s (2002) input-process-outcome model, examined the effect of positive and negative user judgments on behavior and learning, and expanded the model to include trainee reactions and adaptive transfer. Participants (N = 254) learned about identifying misinformation online by playing Fake It to Make It, a social-impact game that teaches core critical thinking skills. Autoregressive cross-lagged (ARCL) panel analysis was used to analyze and compare models to test the hypothesized relationships among judgments and behavior scores across six game levels in predicting six learning outcomes, including adaptive transfer tasks evaluating online sources. Findings indicated that each judgment was predicted by its own lagged judgment and lagged behavior. Additionally, positive user judgments predicted reactions, post-training self-efficacy, and motivation to transfer, while frustration inhibited declarative knowledge. Results also demonstrated that behavior and declarative knowledge predicted performance on the adaptive transfer tasks. Research recommendations and practice implications are discussed relative to using games to deliver training with emphasis on motivational properties and targeted outcomes. Show less
