BUILDING POLICIES FOR SUPPORTIVE FEEDBACK IN SELF-DIRECTED LEARNING SCENARIOS

Students often face difficulty in self-directed learning scenarios (e.g., studying, research) because they need to control many aspects of the learning session. They need to decide what to learn, how long to perform a learning task, when to shift to a different learning task and manage distractions apart from others. We observed from our previous research that self-reflection and self-evaluation helped students manage their own learning. However, majority of the students only evaluated one or two major aspects of the learning session that they think needed to be changed or improved (e.g., need to spend less time in nonlearning related activities, need to focus on only one learning task at a time). If students would look further into their learning session, they would discover more behaviors that also need to be re-evaluated. In this paper we discussed reinforcement learning-based methods for discovering good learning behavior which can be used by future systems to suggest to students possible ways to improve their behavior.