Human- versus Computer-competitors : Exploring the Relationships between Gamified Competition and Self-regulation in E-learning

Gamified competitions have been introduced in e-learning to motivate learning and decrease drop-out rates. By competing with human or computer competitors, it is expected that learners may achieve higher performance and be less likely to drop out because of the reinforcement of self-regulation. However, it remains unclear whether and how human and computer competitors differently influence self-regulation, restricting e-learning platforms from better deploying these two types of competitors. This research draws on attribution theory and goal orientation literature to explore the relationships between competitor types and self-regulation, as well as the moderating role of goal orientation. System-recorded data from an e-learning platform is used to preliminary examine the hypotheses developed. A lab experiment formally tests the hypotheses. We expect that winning over humans and losing to computers will promote more self-regulation than winning over computers and losing to humans.