Empowering Active Learning to Jointly Optimize System and User Demands
This work addresses user frustration in active learning applications, particularly in education, by integrating user needs into the optimization process, though it is incremental as it builds on existing active learning methods.
The paper tackles the problem of active learning systems frustrating users by labeling irrelevant instances, proposing a joint optimization approach that balances system training efficiency with user utility. In an educational application, the method reduces unsuitable exercises for users while maintaining learning performance.
Existing approaches to active learning maximize the system performance by sampling unlabeled instances for annotation that yield the most efficient training. However, when active learning is integrated with an end-user application, this can lead to frustration for participating users, as they spend time labeling instances that they would not otherwise be interested in reading. In this paper, we propose a new active learning approach that jointly optimizes the seemingly counteracting objectives of the active learning system (training efficiently) and the user (receiving useful instances). We study our approach in an educational application, which particularly benefits from this technique as the system needs to rapidly learn to predict the appropriateness of an exercise to a particular user, while the users should receive only exercises that match their skills. We evaluate multiple learning strategies and user types with data from real users and find that our joint approach better satisfies both objectives when alternative methods lead to many unsuitable exercises for end users.