Which Is More Responsible for Boredom in Intelligent Tutoring Systems: Students (Trait) or Problems (State)?

Boredom is unpleasant, and has been repeatedly shown to be associated with poor performance and long-term disengagement in educational contexts. Boredom is prevalent within a range of online learning environments, has been shown to correlate negatively with learning in those environments, and often precedes disengaged behaviors such as off-task behavior and gaming the system. Therefore, it is important to identify the causes of boredom in these environments. In psychology research, there is ongoing debate about the degree to which individual students are prone to boredom ("trait" explanations) or the degree to which boredom is driven by state-based factors, such as the design of the learning environment. In this study, we apply an unobtrusive computational detector of student boredom to log data from an intelligent tutoring system to determine whether state or trait factors better predict the prevalence of boredom in students using that system. Knowing which type of factor better predicts boredom in a specific system can help us to narrow down further research on why boredom occurs and what steps should be taken to mitigate boredom's negative effects.